OpenAI positions model size and reasoning as separate optimization axes

Sanders frames model selection as a portfolio optimization problem rather than a capability ladder. The implicit message: customers no longer chase the largest model; they now pick orthogonal levers (size, reasoning compute, orchestration) based on workload. This is a deliberate repositioning that justifies why smaller, cheaper models like Terra can coexist with larger ones without cannibalizing revenue.

This articulation arrives just after Terra's launch and pricing cuts (late October), which already signaled cost-as-competitive-weapon. Sanders' rule of thumb is the narrative scaffolding for that strategy. It also echoes the deployment-constraint logic from the time-series study (arXiv, late October), which showed that real-world constraints invert benchmark rankings. OpenAI is essentially saying: we've learned what practitioners already knew. The Hebbia evaluation of Astra reinforces this by showing differentiation happens on structured output and retrieval, not raw scale. Together, these moves suggest OpenAI is moving from 'bigger is better' to 'fit is everything.'

If OpenAI publishes domain-specific benchmarks (legal, medical, financial) where Terra outperforms larger models in the next two quarters, the portfolio strategy is real. If instead they continue releasing larger models without explicit guidance on when to use smaller ones, Sanders' framework was positioning theater. Watch whether enterprise customers actually adopt Terra for production workloads at scale; that's the proof.